app.py

import gradio as gr
import numpy as np
import random
import os

# import spaces #[uncomment to use ZeroGPU]
from diffusers import DiffusionPipeline
from peft import PeftModel, LoraConfig
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

if torch.cuda.is_available():
    torch_dtype = torch.float16
else:
    torch_dtype = torch.float32

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024

LoRA_path = 'new_model'

# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
    model_id,
    prompt,
    negative_prompt,
    seed,
    randomize_seed,
    width,
    height,
    guidance_scale,
    num_inference_steps,
    progress=gr.Progress(track_tqdm=True),
):
    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    if model_id == 'Maria_Lashina_LoRA':
        adapter_name = 'a cartoonish mouse'
        unet_sub_dir = os.path.join(LoRA_path, "unet")
        text_encoder_sub_dir = os.path.join(LoRA_path, "text_encoder")
        
        pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype).to(device)
        pipe.unet = PeftModel.from_pretrained(pipe.unet, unet_sub_dir, adapter_name=adapter_name)

        pipe.text_encoder = PeftModel.from_pretrained(pipe.text_encoder, text_encoder_sub_dir, adapter_name=adapter_name)

        if torch_dtype == torch.float16:
            pipe.unet.half()
            pipe.text_encoder.half()

        pipe.to(device)
    
    else:
        pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype).to(device)

    image = pipe(
        prompt=prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator,
    ).images[0]

    return image, seed


examples = [
    "The image of a cartoonish mouse eating from a red bowl of yellow triangle chips, her cheeks are full. The mouse is gray with big pink ears, small white eyes and a black pointed nose. It has a simple design, the background color is white. The style of the image is reminiscent of a sticker or a digital illustration.",
    "The image of a cartoonish mouse with red hearts instead of eyes meaning that the mouse is in love with something. The mouse is gray with big pink ears and a black pointed nose. It has a simple design, the background color is white. The style of the image is reminiscent of a sticker or a digital illustration.",
    "The image of a cartoonish mouse with sunglasses and smiling. The mouse is gray with big pink ears and a black pointed nose. It has a simple design, the background color is white. The style of the image is reminiscent of a sticker or a digital illustration.",
]

css = """
#col-container {
    margin: 0 auto;
    max-width: 640px;
}
"""

with gr.Blocks(css=css) as demo:
    with gr.Column(elem_id="col-container"):
        gr.Markdown(" # Text-to-Image Gradio Template")

        MODEL_LIST = [
            "CompVis/stable-diffusion-v1-4",
            "stable-diffusion-v1-5/stable-diffusion-v1-5",
            "Maria_Lashina_LoRA"
        ]
        with gr.Row():
            model_id = gr.Dropdown(
                label="Model",
                choices=MODEL_LIST
            )

        with gr.Row():
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )

            run_button = gr.Button("Run", scale=0, variant="primary")

        result = gr.Image(label="Result", show_label=False)

        with gr.Accordion("Advanced Settings", open=False):
            negative_prompt = gr.Text(
                label="Negative prompt",
                max_lines=1,
                placeholder="Enter a negative prompt",
                visible=False,
            )

            seed = gr.Slider(
                label="Seed",
                minimum=0,
                maximum=MAX_SEED,
                step=1,
                value=42,
            )

            randomize_seed = gr.Checkbox(label="Randomize seed", value=False)

            with gr.Row():
                width = gr.Slider(
                    label="Width",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,  # Replace with defaults that work for your model
                )

            with gr.Row():
                guidance_scale = gr.Slider(
                    label="Guidance scale",
                    minimum=0.0,
                    maximum=10.0,
                    step=0.1,
                    value=7.0,  # Replace with defaults that work for your model
                )

                num_inference_steps = gr.Slider(
                    label="Number of inference steps",
                    minimum=1,
                    maximum=50,
                    step=1,
                    value=20,  # Replace with defaults that work for your model
                )

        gr.Examples(examples=examples, inputs=[prompt])
    gr.on(
        triggers=[run_button.click, prompt.submit],
        fn=infer,
        inputs=[
            model_id,
            prompt,
            negative_prompt,
            seed,
            randomize_seed,
            width,
            height,
            guidance_scale,
            num_inference_steps,
        ],
        outputs=[result, seed],
    )

if __name__ == "__main__":
    demo.launch()